Wide-area agricultural monitoring and prediction

ABSTRACT

Ground-based measurements of agricultural metrics such as NDVI are used to calibrate wide-area aerial measurements of the same metrics. Calibrated wide-area data may then be used as an input to a field prescription processor.

TECHNICAL FIELD

The disclosure is related to monitoring and prediction of agriculturalperformance over wide areas.

BACKGROUND

A modern crop farm may be thought of as a complex biochemical factoryoptimized to produce corn, wheat, soybeans or countless other products,as efficiently as possible. The days of planting in spring and waitinguntil fall harvest to assess results are long gone. Instead, today'sbest farmers try to use all available data to monitor and promote plantgrowth throughout a growing season. Farmers influence their cropsthrough the application of fertilizers, growth regulators, harvest aids,herbicides and pesticides. Precise crop monitoring—to help decidequantity, location and timing of field applications—has a profoundeffect on cost, crop yield and pollution.

Normalized difference vegetative index (NDVI) is an example of a popularcrop metric. NDVI is based on differences in optical reflectivity ofplants and dirt at different wavelengths. Dirt reflects more visible(VIS) red light than near-infrared (NIR) light, while plants reflectmore NIR than VIS. Chlorophyll in plants is a strong absorber of visiblered light; hence, plants'characteristic green color.

${{NDVI} = \frac{r_{NIR} - r_{VIS}}{r_{NIR} + r_{VIS}}},$

where r is reflectivity measured at the wavelength indicated by thesubscript. Typically, NIR is around 770 nm while VIS is around 660 nm.In various agricultural applications, NDVI correlates well with biomass,plant height, nitrogen content or frost damage.

Farmers use NDVI measurements to decide when and how much fertilizer toapply. Early in a growing season it may be hard to gauge how muchfertilizer plants will need over the course of their growth. Too late inthe season, the opportunity to supply missing nutrients may be lost.Thus the more measurements are available during a season, the better.

A crop's yield potential is the best yield obtainable for a particularplant type in a particular field and climate. Farmers often apply a highdose of fertilizer, e.g. nitrogen, to a small part of a field, theso-called “N-rich strip”. This area has enough nitrogen to ensure thatnitrogen deficiency does not retard plant growth. NDVI measurements onplants in other parts of the field are compared with those from theN-rich strip to see if more nitrogen is needed to help the field keep upwith the strip.

The consequences of applying either too much or too little nitrogen to afield can be severe. With too little nitrogen the crop may not achieveits potential and profit may be left “on the table.” Too much nitrogen,on the other hand, wastes money and may cause unnecessary pollutionduring rain runoff. Given imperfect information, farmers tend to overapply fertilizer to avoid the risk of an underperforming crop. Thus,more precise and accurate plant growth measurements save farmers moneyand prevent pollution by reducing the need for over application.

NDVI measurements may be obtained from various sensor platforms, eachwith inherent strengths and weaknesses. Satellite or aerial imaging canquickly generate NDVI maps that cover wide areas. However, satellitesdepend on the sun to illuminate their subjects and the sun is rarely, ifever, directly overhead a field when a satellite acquires an image.Satellite imagery is also affected by atmospheric phenomena such asclouds and haze. These effects lead to an unknown bias or offset in NDVIreadings obtained by satellites or airplanes. Relative measurementswithin an image are useful, but comparisons between images, especiallythose taken under different conditions or at different times, may not bemeaningful.

Local NDVI measurements may be obtained with ground based systems suchas the Trimble Navigation “GreenSeeker”. A GreenSeeker is an activesensor system that has its own light source that is scannedapproximately one meter away from plant canopy. The light source ismodulated to eliminate interference from ambient light. Visible andnear-infrared reflectivity are measured from illumination that isscanned over a field. Ground-based sensors like the GreenSeeker can bemounted on tractors, spray booms or center-pivot irrigation booms toscan an entire field. (GreenSeekers and other ground-based sensors mayalso be hand-held and, optionally, used with portable positioning anddata collection devices such as laptop computers, portable digitalassistants, smart phones or dedicated data controllers.) Active,ground-based sensors provide absolute measurements that may be comparedwith other measurements obtained at different times, day or night. Itdoes take time, however, to scan the sensors over fields of interest.

What is needed are wide area plant monitoring systems and methodscapable of providing absolute data that can be compared with other dataobtained by different methods and/or at different times. Furthermore,farmers need help navigating the vast stores of potentially valuabledata that affect plant growth.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic map of nine farm fields with management zones.

FIG. 2 shows one of the fields of FIG. 1 in greater detail.

FIG. 3 shows a schematic satellite image of the fields of FIG. 1.

FIG. 4 shows a block diagram of a wide-area field prescription system.

FIG. 5 shows a block diagram of a method to combine satellite and grounddata acquired at different times.

FIGS. 6A and 6B show a schematic graph of NDVI data obtained atdifferent times via different methods.

DETAILED DESCRIPTION

Wide-area agricultural monitoring and prediction encompasses systems andmethods to generate calibrated estimates of plant growth andcorresponding field prescriptions. Data from ground and satellite basedsensors are combined to obtain absolute, calibrated plant metrics, suchas NDVI, over wide areas. Further inputs, such as soil, cropcharacteristics and climate data, are stored in a database. A processoruses the measured plant metrics and database information to createcustomized field prescription maps that show where, when and how muchfertilizer, pesticide or other treatment should be applied to a field tomaximize crop yield.

Ground data are used to remove the unknown bias or offset of satelliteor aerial images thereby allowing images taken at different times to becompared with each other or calibrated to an absolute value. Soil, cropand climate data may also be stored as images or maps. The volume ofdata stored in the database can be quite large depending on the area ofland covered and the spatial resolution. Simulations of plant growth maybe run with plant and climate models to build scenarios such that afarmer can predict not just what may happen to his crops based onaverage assumptions, but also probabilities for outlying events.

A basic ingredient of any field prescription, however, is an accuratemap of actual plant progress measured in the field. NDVI is used here asa preferred example of a metric for measuring plant growth; however,other parameters, such as the green vegetation index, or otherreflectance-based vegetative indices, may also be useful. FIG. 1 shows aschematic map of nine farm fields, 101, 102 . . . 109, delineated bysolid boundary lines. Dashed lines in the figure show the boundaries offield management zones which are labeled by circled numbers 1, 2 and 3.Management zones are areas of common growing characteristics. Qualitiesthat define a zone may include drainage, soil type, ground slope,naturally occurring nutrients, weed types, pests, etc. Regardless of howzones differ, plants within a zone tend to grow about the same. Targetedfertilizer application within a zone can help smooth out growthvariation. Plants in different zones may require markedly differentfertilizer prescriptions.

FIG. 2 shows field 107 of FIG. 1 in greater detail. The field overlapsthree management zones labeled by circled numbers 1, 2 and 3. Path 205shows the track that a ground-based NDVI scanner like a GreenSeekertakes as it measures plant growth in the field. Ground-based scannerscan be deployed on tractors, spray trucks or other equipment and can beprogrammed to record data whenever the equipment moves over a growingarea. (Ground-based scanners may also be hand-held and connected toportable data collection and/or positioning equipment.) Ground-basedscanners are often used for real-time, variable-rate application, butbecause the scanners are automated, they can run any time, not justduring fertilizer application.

In FIG. 2, gray stripe 210 marks the location of an N-rich strip. TheN-rich strip is an area where an excess of nitrogen fertilizer has beenapplied. Plant growth in the N-rich strip is not limited by theavailability of nitrogen, so those plants exhibit the maximum yieldpotential of similar plants in the field: Because N-rich strips areuseful for yield potential calculations, measurement of NDVI in anN-rich strip is often part of a real-time, variable-rate applicationprocedure. N-rich strips are not always needed, however. The performanceof the top 10% of plants in a representative part of a field may providean adequate standard for maximum yield potential, for example.

FIG. 3 shows a schematic satellite image of the fields of FIG. 1. Thearea of land illustrated in FIG. 3 is the same as the area shown inFIG. 1. The land in FIG. 3 has been divided into pixels (e.g. 301, 302,303, 304) similar to those that may be obtained by satellite imaging.FIG. 3 is drawn for purposes of illustration only; it is not to scale.Pixels in an actual satellite image may represent areas in the range ofroughly 1 m² to roughly 100 m². The resolution of today's satelliteimages is suitable for agricultural purposes; it is no longer a limitingfactor as was the case several years ago.

Scale 305 in FIG. 3 is a schematic representation of an NDVI scale.Darker pixels represent higher values of NDVI. Although only fiverelative NDVI levels are shown in FIG. 3, much higher precision isavailable from actual satellite images. Actual satellite images,however, do not provide absolute NDVI with the high accuracy availableusing ground-based sensors. Variations in lighting (i.e. position of thesun), atmospheric effects (e.g. clouds, haze, dust, rain, etc.), andsatellite position all introduce biases and offsets that are difficultto quantify.

It is apparent that NDVI measurements for the set of fields shown inFIGS. 1 and 3 may be obtained by either ground or satellite sensors.Ground measurements provide absolute NDVI at high accuracy whilesatellite measurements provide relative NDVI over wide areas. Whenground and satellite data are available for a common area at times thatare not too far apart, the ground data may be used to resolve theunknown bias or offset in the satellite data. As an example, if field107 in FIG. 1 is measured by a GreenSeeker scan and fields 101 through109 (including 107) are measured by satellite imaging, then overlappingground and satellite data for field 107 can be used to calibrate thesatellite data for all of the fields. The accuracy of ground-based datahas been extended to a wide area. Generally “times that are not too farapart” are within a few days of one another; however, the actual maximumtime difference for useful calibration depends on how fast plants aregrowing. Measurements must be closer together in time for fast-growingcrops. Methods to estimate plant growth rate and extend the amount bywhich ground and satellite measurements can be separated in time arediscussed below.

FIG. 4 shows a block diagram of a wide-area field prescription system.In FIG. 4, ground data 405 and satellite data 410 are inputs to adatabase and processor 430. The output from the database and processoris a field prescription 435; i.e. a plan detailing how much chemicalapplication is needed to optimize yield from a farm field. A fieldprescription may be visualized as a map showing when, where and how muchfertilizer or pesticide is required on a field. The prescription may beused by an automated application system such as a spray truck withdynamically controllable spray nozzles.

Soil data 415, crop data 420 and climate data 425 may also be inputs tothe database and processor although not all of these data may be neededfor every application. All of the data sources 405 through 425, andother data not shown, are georeferenced. Each data point (soil type,crop type, climate history, NDVI from various sources, etc.) isassociated with a location specified in latitude and longitude or anyother convenient mapping coordinate system. The various data may besupplied at different spatial resolution. Climate data, for example, islikely to have lower spatial resolution than soil type.

Data inputs 405 through 425 are familiar to agronomists as inputs toplant yield potential algorithms. Database and processor 430 are thuscapable of generating wide-area field prescriptions based on any ofseveral possible plant models and algorithms. The ability to rundifferent hypothetical scenarios offers farmers a powerful tool toassess the risks and rewards of various fertilizer or pesticideapplication strategies. For example, a farmer might simulate theprogress of one of his fields given rainfall and growing degree dayscenarios representing average growing conditions and also growingconditions likely to occur only once every ten years. Furthermore, thefarmer may send a ground-based NDVI sensor to scan small parts of just afew of his fields frequently, perhaps once a week, for example. Thesesmall data collection areas may then be used to calibrate satellite datacovering a large farm. The resulting calibrated data provides the farmerwith more precise estimates of future chemical needs and reduces cropyield uncertainty.

It is rarely possible to obtain ground and satellite NDVI data measuredat the same time. If only a few days separate the measurements, theresulting errors may be small enough to ignore. However, better resultsmay be obtained by using a plant growth model to propagate data forwardor backward in time as needed to compare asynchronous sources. FIG. 5shows a block diagram of a method to combine satellite and ground dataacquired at different times.

In FIG. 5, ground data 505, e.g. NDVI obtained by a GreenSeeker, andsatellite data 510 are inputs to a plant growth model 515. Results fromthe model are used to generate an NDVI map 520 for any desired time.Most plants' growth is described approximately by a sigmoid function;the part of the sigmoid of interest to farmers is the main growth phasewhich is approximately exponential. Furthermore, for data not separatedtoo far in time, plants' exponential growth may be approximated by alinear growth model.

The use of a linear plant growth model to compare asynchronousground-based and satellite measurements of NDVI may be understood byreferring to FIGS. 6A and 6B that show a schematic graph of NDVI dataobtained at different times via different methods. In FIG. 6A NDVI isplotted versus time for a small area, for example a single data point ina farm field, or a small section of a field. NDVI measurements 605 and610 are obtained by a ground-based system at times t₁ and t₂respectively, while NDVI measurement 614 is obtained from a satelliteimage at a later time t₃. Satellite-derived data point 614 has a bias oroffset. The bias in data point 614 may be calculated by fitting line 620to ground-derived data points 605 and 610. The result is that the actualNDVI measured by the satellite at time t₃ (for the specific ground areaunder consideration in FIG. 6A) is represented by data point 616, thevalue of the function represented by line 620 at t₃. Of course, thelonger the interval between t₂ and t₃, the less confidence may be placedin linear extrapolation 620. However, the result is likely more accuratethan simply forcing data point 614 to have the same value as data point610, for example.

The situation plotted in FIG. 6B is similar to that of FIG. 6A exceptfor the order in which data is obtained. In FIG. 6B NDVI measurements625 and 635 are obtained by a ground-based system at times t₄ and t₆respectively, while NDVI measurement 628 is obtained from a satelliteimage at an intermediate time t₅. Satellite-derived data point 628 has abias or offset. The bias in data point 628 may be calculated by fittingline 640 to ground-derived data points 625 and 635. The result is thatthe actual NDVI measured by the satellite at time t₅ (for the specificground area under consideration in FIG. 6B) is represented by data point632, the value of the function represented by line 640 at t₅.

FIGS. 6A and 6B have been described in a simplified scenario in whichplant growth is assumed to be easily modeled as a function of time.However, it may be more realistic to express plant growth as a functionof heat input, represented for example by growing degree days sinceplanting. If the number of growing degree days per actual day does notchange (an idealized and somewhat unlikely scenario), then plant growthversus time or heat input will have the same functional form. Ingeneral, the time axis in FIGS. 6A and 6B may be replaced by a modelwhich may include heat input, moisture, rainfall, sunlight intensity orother data that affect growth rate.

It will be apparent to those skilled in the art that the methodsdiscussed above in connection with FIGS. 5 and 6 may be generalized. Twomeasurement sources—ground and satellite sensors—measure the samequantity. One source provides absolute measurements while the otherincludes an unknown bias. A linear model may be used for the timeevolution of the measured quantity, NDVI. The situation is well suitedfor the application of a digital filter, such as a Kalman filter, toobtain an optimal estimate for NDVI. Relative measurements of NDVI overwide areas are calibrated by absolute measurements over smaller, subsetareas.

Sparse spatial NDVI sampling may be sufficient to calibrate wide-areasatellite data. More dense sampling is needed for smaller managementzones which are often associated with more rapidly varying topography,while less dense sampling is sufficient for larger management zoneswhich are often associated with flatter topography.

The wide-area agricultural and prediction systems and methods describedherein give farmers more precise and accurate crop information overwider areas than previously possible. This information may be combinedwith soil, climate, crop and other spatial data to generate fieldprescriptions using standard or customized algorithms.

Although many of the systems and methods have been described in terms offertilizer application, the same principles apply to pesticide,herbicide and growth regulator application as well. Although many of thesystems and methods have been described as using images obtained fromsatellites, the same principles apply to images obtained from airplanes,helicopters, balloons, unmanned aerial vehicles (UAVs) and other aerialplatforms. Thus “aerial data” comprises data obtained from satellite,airplane, helicopter, balloon and UAV imaging platforms. Similarly,“ground-based data” comprises data obtained from sensors that may bemounted on a truck, tractor or other vehicle, or that may be hand-held.Although many of the systems and methods have been described in terms ofNDVI, other reflectance-based vegetative indices may be used.

The above description of the disclosed embodiments is provided to enableany person skilled in the art to make or use the disclosure. Variousmodifications to these embodiments will be readily apparent to thoseskilled in the art, and the principles defined herein may be applied toother embodiments without departing from the scope of the disclosure.Thus, the disclosure is not intended to be limited to the embodimentsshown herein but is to be accorded the widest scope consistent with theprinciples and novel features disclosed herein.

1. A method for calibrating agricultural measurements comprising:obtaining aerial data representing relative measurements of anagricultural metric in a geographic area, the relative measurementshaving an unknown bias; obtaining ground-based data representingabsolute measurements of the agricultural metric within the geographicarea; and, using the absolute measurements to calibrate the relativemeasurements, thereby synthesizing absolute measurements of theagricultural metric in parts of the geographic area.
 2. The method ofclaim 1, the aerial data obtained from a satellite.
 3. The method ofclaim 1, the aerial data obtained from an airplane.
 4. The method ofclaim 1, the agricultural metric being normalized difference vegetativeindex.
 5. The method of claim 1, the agricultural metric being areflectance-based vegetative index.
 6. The method of claim 1 furthercomprising: combining data representing the ground-based and synthesizedabsolute measurements with additional spatial agricultural data togenerate a prescription for the application of chemicals to anagricultural field.
 7. The method of claim 6, the additional spatialagricultural data being a soil data map.
 8. The method of claim 6, theadditional spatial agricultural data being a crop data map.
 9. Themethod of claim 6, the additional spatial agricultural data beingclimate data.
 10. The method of claim 6, the chemicals beingfertilizers.
 11. The method of claim 6, the chemicals being pesticidesor herbicides.
 12. The method of claim 6, the prescription based on anagricultural algorithm having an agricultural metric and climate data asinputs.
 13. The method of claim 12, the agricultural metric beingnormalized difference vegetative index and the climate data includinggrowing degree days since planting.
 14. The method of claim 1, thesynthesizing absolute measurements including using a plant growth modelto propagate ground-based data forward or backward in time as needed tocompare it with non-contemporaneous satellite data.
 15. The method ofclaim 14, the plant growth model being a linear model.
 16. A system formaking calibrate agricultural measurements comprising: a source ofaerial data representing relative measurements of an agricultural metricin a geographic area, the relative measurements having an unknown bias;a source of ground-based data representing absolute measurements of theagricultural metric within the geographic area; and, a database andprocessor that use the absolute measurements to calibrate the relativemeasurements, thereby synthesizing absolute measurements of theagricultural metric in parts of the geographic area.
 17. The system ofclaim 16, the aerial data obtained from a satellite.
 18. The system ofclaim 16, the aerial data obtained from an airplane.
 19. The system ofclaim 16, the agricultural metric being normalized difference vegetativeindex.
 20. The system of claim 16, the agricultural metric being areflectance-based vegetative index.
 21. The system of claim 16, thedatabase and processor further combining data representing theground-based and synthesized absolute measurements with additionalspatial agricultural data to generate a prescription for the applicationof chemicals to an agricultural field.
 22. The system of claim 16, theadditional spatial agricultural data being a soil data map.
 23. Thesystem of claim 15, the additional spatial agricultural data being acrop data map.
 24. The system of claim 16, the additional spatialagricultural data being climate data.
 25. The system of claim 16, thechemicals being fertilizers.
 26. The system of claim 16, the chemicalsbeing pesticides or herbicides.
 27. The system of claim 16, theprescription based on an agricultural algorithm having an agriculturalmetric and climate data as inputs.
 28. The system of claim 27, theagricultural metric being normalized difference vegetative index and theclimate data including growing degree days since planting.
 29. Thesystem of claim 16, the synthesizing absolute measurements includingusing a plant growth model to propagate ground-based data forward orbackward in time as needed to compare it with non-contemporaneoussatellite data.
 30. The system of claim 16, the plant growth model beinga linear model.